New Threat Model: AI Privacy & Dataset Security

[sakshipimpale33]

Summary:
AI applications often require large datasets for training, which may include sensitive personal information. If unauthorized access, misuse, or improper handling of this data occurs, it can lead to privacy breaches and security vulnerabilities. I propose adding a new AI privacy threat model to the OWASP AI Security project to address these risks.

Threat Details:
Risk: AI datasets may contain personally identifiable information (PII) or confidential data that, if exposed, can lead to security and privacy concerns.
Attack Scenarios:
Unauthorized Data Access: Attackers gain access to sensitive datasets used in AI training.
Data Poisoning Attacks: Malicious actors modify datasets to introduce bias or vulnerabilities.
Unprotected Dataset Storage: AI training data is stored in publicly accessible locations without proper encryption or access controls.
Proposed Mitigations:
Access Control Mechanisms: Implement strong authentication and role-based access control (RBAC) for AI datasets.
Data Anonymization: Apply differential privacy techniques before using datasets in AI models.
Secure Storage: Ensure dataset encryption and proper security measures in cloud-based and local AI environments.

Next Steps:
Would love to get feedback from the OWASP team on whether this fits within the AI Security Project.
If approved, I’d be happy to contribute by:
✅ Writing documentation on AI dataset security risks.
✅ Adding this as a new OWASP AI Security threat model.
✅ Implementing best practices in the OWASP Threat Modeling project.

Looking forward to hearing your thoughts!